diff --git a/paper/sections/06-results.tex b/paper/sections/06-results.tex new file mode 100644 index 0000000000000000000000000000000000000000..d3a0919d51340ab78ff0d7523a1a1a74c43e3b19 --- /dev/null +++ b/paper/sections/06-results.tex @@ -0,0 +1,51 @@ +\subsection{GPS sensor dynamics} +\label{sec:res:gps} + +First-principle analysis of GPS dynamics: \emph{time to first + fix}. Comparison with empirical analysis from the state of the art +(check that numbers match the python-nokia implementation or whatever +else is available). Implementation issues with existing solutions +(there are some unjustified delays -- probably introduced by the +software and software bugs -- that could be eliminated). + +Additionally, decribe \emph{phenomena} like loss of ephemeris and +randing data and what are the delays introduced because of that. Say +that losing the ephemeris data means basically having the GPS receiver +turned off for ``too long'' and losing the ranging data is mostly +equivalent to a worst case in losing visibility of the satellites. If +you want to distinguish, you can have a finite state machine for each +satellite. + +\subsection{Power Consumption Accuracy Trade Off} +\label{sec:res:tradeoff} + +In this section, we use real traces from an IMU sensor and a GPS +receiver in two different conditions: car, and bicycle. In both cases, +we recorded measurements for the entire duration of the trace with +both devices. We show the accuracy of the IMU, compared to the GPS +trace (which the sensor fusion algorithm considers to be the ground +truth). We expose the trade off between power consumption due to the +GPS antenna being turned on and accuracy in both cases. Expectation: +in the bike trace, the IMU sensor trace is more noisy. + +Figures (both bike and car) with the accuracy ``areas''. + +\subsection{Simulation of Ranging Data Loss} +\label{sec:res:vis} + +Simulation of what happens if ``lose visibility'' transition is taken +from time to time on one of the two traces above. + +\subsection{Simulation Results} +\label{sec:res:sim} + +Montecarlo simulations. Characteristics: +\begin{itemize} +\item We generate 10000 traces, 60 minutes long. +\item For each point in each trace, we randomly extract from + probability distributions the visibility of satellites. We also + randomize the time to fetch signals. +\item Figure comparing clouds of points with only GPS and GPS+IMU in + the axis \emph{accuracy} (sum of distances from the ideal GPS trace) + and \emph{power consumption} (due to antenna). +\end{itemize} \ No newline at end of file